Converting List to Dataframe for positioning position in Python
As a cryptocurrency trader, having accurate and organized data is crucial for making knowledge. In this article, we will explore how to convert a price list from the Binance Futures API in a Pandas datoframe, which can be used to manage the position.
Premises:
- Install
binance_flibrary using PIP:PIP Install binance_f
- Configure the API Binance credentials
- Import the necessary libraries and set -va API key
Code:
`Python
from Binance_f Import RequestClient, command card
Configure API credentials and customer instance
api_key = 'your_pi_key'
api_secret = 'your_pi_secret'
Request_client = RequestClient (api_key = api_key, api_secret = api_secret)
Def convert_to_df (prices):
"" ""
Convert a price list to a Pandas dumframe.
Parameters:
Prices (List): List of prices to be converted
Returns:
pd.datrame: Dataframe converted
"" ""
command_book = request_client.get_orderbook ('btcub')
Create a dictionary for storing price and volume data
data = {
"Price": [],
"Volume": []
}
For entry into the order_book.entries:
If you enter.Price> Input. volume:
data ["price"]. Annex (input. Price)
Data ["volume"]. Annex (input. Volume)
DF = pd.dataframe (data)
Return DF
Example of use
Prices = [100.0, 120.0, 110.0, 130.0, 115.0]
Example prices for BTC-USD
DF = convert_to_df (prices)
Print (DF)
Explanation:
- We first import the necessary libraries and configure our API credentials.
- We create an instanceRequestClienta using our key and secret API.
- The
convert_to_df () functiontakes a price list as an input and uses the Binance Futures API to pick up a control card entry for each price.
- For each input, add the price and volume data to a dictionary (“data”).
- We create a Datoframe Pandas from the dictionary and return it.
- In the section of use of the example, we demonstrate how to use
convert_to_df ()with a price list.
Tips and Variations:
- You can change the
convert_to_df ()`to host different types of prices (for example, candles).
- If you need to process additional data (for example, tendency analysis), you may want to use a more advanced library, such as “Pandas-Dareder”.
- To optimize performance, consider the cache memory of API requests or the use of a tail -based approach for large volume data management.
Following this article and adapting it to your specific needs, you can efficiently convert the price list to a Pandas Datoframe for positioning position in Python.